Two Separate Brain Networks for Predicting Trainability and Tracking Training-Related Plasticity in Working Dogs.

canine comparative biology dog functional MRI functional connectivity resting state

Journal

Animals : an open access journal from MDPI
ISSN: 2076-2615
Titre abrégé: Animals (Basel)
Pays: Switzerland
ID NLM: 101635614

Informations de publication

Date de publication:
02 Apr 2024
Historique:
received: 29 02 2024
revised: 28 03 2024
accepted: 29 03 2024
medline: 13 4 2024
pubmed: 13 4 2024
entrez: 13 4 2024
Statut: epublish

Résumé

Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months' post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks.

Identifiants

pubmed: 38612321
pii: ani14071082
doi: 10.3390/ani14071082
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Auburn University's Intramural Grant; the Defense Advanced 661 Research Projects Agency
ID : W911QX-13-C-0123

Auteurs

Gopikrishna Deshpande (G)

Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA.
Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA.
Center for Neuroscience, Auburn University, Auburn, AL 36849, USA.
Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore 560029, India.
Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad 502285, India.

Sinan Zhao (S)

Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.

Paul Waggoner (P)

Canine Performance Sciences Program, College of Veterinary Medicine, Auburn University, Auburn, AL 36849, USA.

Ronald Beyers (R)

Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.

Edward Morrison (E)

Department of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA.

Nguyen Huynh (N)

Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.

Vitaly Vodyanoy (V)

Department of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA.

Thomas S Denney (TS)

Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA.
Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA.
Center for Neuroscience, Auburn University, Auburn, AL 36849, USA.

Jeffrey S Katz (JS)

Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA.
Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA.
Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA.
Center for Neuroscience, Auburn University, Auburn, AL 36849, USA.

Classifications MeSH